1. Info Vis: Multi-Dimensional Data Chris North cs3724: HCI

2. Presentations jerome holman john gibson Vote: UI Hall of Fame/Shame?

3. Quiz Why visualization? Class motto:

4. Visualization Design Principles

5. Increase Data Density Calculate data/pixel “A pixel is a terrible thing to waste.”

6. Eliminate “Chart Junk” How much “ink” is used for non-data? Reclaim empty space (% screen empty) Attempt simplicity (e.g. am I using 3d just for coolness?)

7. Information Visualization Mantra Overview first, zoom and filter, then details on demand

8. InfoVis Design Principles Increase data density Eliminate “chart junk” Mantra: Overview first, zoom&filter, details on demand Insight factor Does the design reveal the data? Does the design help me explore, learn, understand? Show me the data!

9. Visualizing Multi-dimensional data

10. Multi-dimensional Data Table Attributes (aka: dimensions, fields, variables, columns, …) Items (aka: data points, records, tuples, rows, …) Data Values Data Types: Quantitative Ordinal Categorical/Nominal

11. Basic Visualization Model Data Visualization Visual Mapping Interaction

12. Visual Mapping 1.Map: data items  visual marks Visual marks: Points Lines Areas Volumes

13. Visual Mapping 1.Map: data items  visual marks 2.Map: data item attributes  visual mark attributes Visual mark attributes: Position, x, y Size, length, area, volume Orientation, angle, slope Color, gray scale, texture Shape

14. Example Hard drives for sale: price ($), capacity (MB), quality rating (1-5) p c

15. Example: Spotfire Film database Year  X Length  Y Popularity  size Subject  color Award?  shape

16. Ranking Visual Attributes 1.Position 2.Length 3.Angle, Slope 4.Size 5.Color Increased accuracy for quantitative data -W.S. Cleveland Color better for categorical data -J. Mackinlay

17. Basic Charts…

18. Factors in Visualization Design User tasks Data Data scale: # recs # attrs # possible data values

19. Data Scale # of attributes (dimensionality) # of items # of possible values (e.g. bits/value)

20. Spotfire Multiple views: brushing and linking Dynamic Queries Details window

21. TableLens (Eureka by Inxight) Visual encoding of cell values, sorting Details expand within context

22. Parallel Coordinates Bag cartesian orthogonal layout Parallel axes Data point = connected line segment (0, 1, -1, 2) = 0 x 0 y 0 z 0 w

23. Parallel Coordinates (XmdvTool)

24. Parallel Coordinates

25. Info. Vis. Topics Information types: Multi-dimensional: databases,… 1D, 2D, 3D Trees, Graphs Text, document collections Interaction strategies: Overview+Detail Focus+Context Zooming How (not) to lie with visualization

26. Homework #2: Info. Vis. Tools Get some data: Tabular, >=5 attributes (columns), >=500 items (rows) Use 2 visualization tools + Excel: Spotfire, TableLens, Parallel Coordinates Mcbryde 104c 2 page report: Discoveries in data Comparison of tools Due: Feb 19: A-K Feb 21: L-Z

27. Project 2: Java 3 students per team Ambitious project 0: form team (feb 14) 1: design(feb 28) 2: initial implementation(mid march) 3: final implementation (end march)

28. Next Presentations: proj1 design, UI critique Thurs: john randal, tom shultz Next Tues: mohamed hassoun, aaron dalton Next Thurs: nadine edwards, steve terhar